Diagnostic and prognostic values of the QRS-T angle in patients with suspected acute decompensated heart failure

Limits of the spatial ventricular gradient and QRST angles in patients with normal electrocardiograms and no known cardiovascular disease stratified by age, sex, and race

INTRODUCTION

Measurement of the spatial ventricular gradient (SVG), spatial QRST angles, and other vectorcardiographic measures of myocardial electrical heterogeneity have emerged as novel risk stratification methods for sudden cardiac death and other adverse cardiovascular events. Prior studies of normal limits of these measurements included primarily young, healthy, White volunteers, but normal limits in older patients are unknown. The influence of race and body mass index (BMI) on these measurements is also unclear.

METHODS

Normal 12-lead electrocardiograms (ECGs) from a single center were identified. Patients with abnormal cardiovascular, pulmonary, or renal history (assessed by International Classification of Disease [ICD-9/ICD-10] codes) or abnormal cardiovascular imaging were excluded. The SVG and QRST angles were measured and stratified by age, sex, and race. Multivariable linear regression was used to assess the influence of age, BMI, and heart rate (HR) on these measurements.

RESULTS

Among 3292 patients, observed ranges of SVG and QRST angles (peak and mean) differed significantly based on sex, age, and race. Sex differences attenuated with increasing age. Men tended to have larger SVG magnitude (60.4 [46.1-77.8] vs. 52.5 [41.3-65.8] mv*ms, p < .0001) and elevation, and more anterior/negative SVG azimuth (-14.8 [-25.1 to -4.3] vs. 1.3 [-9.8 to 10.5] deg, p < .0001) compared to women. Men also had wider QRST angles. Observed ranges varied significantly with BMI and HR. SVG and QRST angle measurements were robust to different filtering bandwidths and moderate fiducial point annotation errors, but were heavily affected by changes in baseline correction.

CONCLUSIONS

Age, sex, race, BMI, and HR significantly affect the range of SVG and QRST angles in patients with normal ECGs and no known cardiovascular disease, and should be accounted for in future studies. An online calculator for prediction of these "normal limits" given demographics is provided at https://bivectors.github.io/gehcalc/.

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy

AIMS

This study aims to identify and visualize electrocardiogram (ECG) features using an explainable deep learning-based algorithm to predict cardiac resynchronization therapy (CRT) outcome. Its performance is compared with current guideline ECG criteria and QRSAREA.

METHODS AND RESULTS

A deep learning algorithm, trained on 1.1 million ECGs from 251 473 patients, was used to compress the median beat ECG, thereby summarizing most ECG features into only 21 explainable factors (FactorECG). Pre-implantation ECGs of 1306 CRT patients from three academic centres were converted into their respective FactorECG. FactorECG predicted the combined clinical endpoint of death, left ventricular assist device, or heart transplantation [c-statistic 0.69, 95% confidence interval (CI) 0.66-0.72], significantly outperforming QRSAREA and guideline ECG criteria [c-statistic 0.61 (95% CI 0.58-0.64) and 0.57 (95% CI 0.54-0.60), P < 0.001 for both]. The addition of 13 clinical variables was of limited added value for the FactorECG model when compared with QRSAREA (Δ c-statistic 0.03 vs. 0.10). FactorECG identified inferolateral T-wave inversion, smaller right precordial S- and T-wave amplitude, ventricular rate, and increased PR interval and P-wave duration to be important predictors for poor outcome. An online visualization tool was created to provide interactive visualizations (https://crt.ecgx.ai).

CONCLUSION

Requiring only a standard 12-lead ECG, FactorECG held superior discriminative ability for the prediction of clinical outcome when compared with guideline criteria and QRSAREA, without requiring additional clinical variables. End-to-end automated visualization of ECG features allows for an explainable algorithm, which may facilitate rapid uptake of this personalized decision-making tool in CRT.

Intra-subject stability of different expressions of spatial QRS-T angle and their relationship to heart rate

Three-dimensional angle between the QRS complex and T wave vectors is a known powerful cardiovascular risk predictor. Nevertheless, several physiological properties of the angle are unknown or poorly understood. These include, among others, intra-subject profiles and stability of the angle relationship to heart rate, characteristics of angle/heart-rate hysteresis, and the changes of these characteristics with different modes of QRS-T angle calculation. These characteristics were investigated in long-term 12-lead Holter recordings of 523 healthy volunteers (259 females). Three different algorithmic methods for the angle computation were based on maximal vector magnitude of QRS and T wave loops, areas under the QRS complex and T wave curvatures in orthogonal leads, and weighted integration of all QRS and T wave vectors moving around the respective 3-dimensional loops. These methods were applied to orthogonal leads derived either by a uniform conversion matrix or by singular value decomposition (SVD) of the original 12-lead ECG, giving 6 possible ways of expressing the angle. Heart rate hysteresis was assessed using the exponential decay models. All these methods were used to measure the angle in 659,313 representative waveforms of individual 10-s ECG samples and in 7,350,733 individual beats contained in the same 10-s samples. With all measurement methods, the measured angles fitted second-degree polynomial regressions to the underlying heart rate. Independent of the measurement method, the angles were found significantly narrower in females (p &lt; 0.00001) with the differences to males between 10o and 20o, suggesting that in future risk-assessment studies, different angle dichotomies are needed for both sexes. The integrative method combined with SVD leads showed the highest intra-subject reproducibility (p &lt; 0.00001). No reproducible delay between heart rate changes and QRS-T angle changes was found. This was interpreted as a suggestion that the measurement of QRS-T angle might offer direct assessment of cardiac autonomic responsiveness at the ventricular level.

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector's coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.

[Factors associated with an increase in spatial and frontal QRS-T angles in patients with anterior myocardial infarction]

Aim    To determine existence of a relationship between any clinical, echocardiographic and coronarographic factors and increased spatial QRS-T (sQRS-T) angle and frontal QRS-T (fQRS-T) angle in patients with anterior myocardial infarction.Material and methods    This study included 137 patients aged 62 [53; 72] years with anterior acute myocardial infarction managed at the A.L. Myasnikov Institute of Clinical Cardiology. fQRS-T was calculated as the module of difference between the frontal plane QRS complex axis and the T wave axis. sQRS-T was calculated as a spatial angle between QRS and T integral vectors from a synthesized vectorcardiogram.Results    fQRS-T values for a group (median [25th; 75th percentile]) were 81 [37; 120]°; sQRS-T values were 114 [80; 141]°. The correlation coefficient between fQRS-T and sQRS-T values was 0.41 (p&lt;0.001). fQRS-T weakly but statistically significantly correlated with patients' age (r=0.28; p=0.001), left ventricular ejection fraction (LV EF, r= -0.22; p=0.01), and glomerular filtration rate (r=-0.32; p=0.0002). sQRS-T weakly but statistically significantly correlated with left ventricular end-diastolic dimension (r=0.24; p=0.0048), LV EF (r=-0.28; p=0.0009), and the number of affected segments according to echocardiography data (r=0.27; p=0.002). fQRS-T values were significantly higher in the presence of concurrent arterial hypertension. sQRS-T values were significantly higher in the presence of a history of chronic heart failure. Both fQRS-T and sQRS-T values increased with increasing number of affected blood vessels and Killip class of acute heart failure.Conclusion    In patients after anterior acute myocardial infarction, increases in fQRS-T and sQRS-T are associated with more severe damage of the vasculature, decreased LV EF, and, thus, more severe clinical course of disease.

Diagnostic and prognostic values of the QRS-T angle in patients with suspected acute decompensated heart failure

AIMS

The aim of this study was to investigate the diagnostic and prognostic utility of the QRS-T angle, an electrocardiogram (ECG) marker quantifying depolarization-repolarization heterogeneity, in patients with suspected acute decompensated heart failure (ADHF).

METHODS AND RESULTS

We prospectively enrolled unselected patients presenting to the emergency department with symptoms suggestive of ADHF. The QRS-T angle was automatically derived from a standard 12-lead ECG recorded at presentation. The primary diagnostic endpoint was a final adjudicated diagnosis of ADHF. The primary prognostic endpoint was all-cause mortality during 2 years of follow-up. Among the 1915 patients enrolled, those with higher QRS-T angles were older, were more commonly male, and had a higher rate of co-morbidities such as arterial hypertension, coronary artery disease, or chronic kidney disease. ADHF was the final adjudicated diagnosis in 1140 (60%) patients. The QRS-T angle in patients with ADHF was significantly larger than in patients with non-cardiac causes of dyspnoea {median 110° [inter-quartile range (IQR) 46-156°] vs. median 33° [IQR 15-57°], P < 0.001}. The diagnostic accuracy of the QRS-T angle as quantified by the area under the receiver operating characteristic curve (AUC) was 0.75 [95% confidence interval (CI) 0.73-0.77, P < 0.001], which was inferior to N-terminal pro-B-type natriuretic peptide (AUC 0.93, 95% CI 0.92-0.94, P < 0.001), but similar to that of high-sensitivity troponin T (AUC 0.78, 95% CI 0.76-0.80, P = 0.09). The AUC of the QRS-T angle for discrimination between ADHF and non-cardiac dyspnoea remained similarly high in subgroups of patients known to be diagnostically challenging, including patients older than 75 years [0.71 (95% CI 0.67-0.74)], renal failure [0.79 (95% CI 0.71-0.87)], and atrial fibrillation at presentation [0.68 (95% CI 0.60-0.76)]. Mortality rates according to QRS-T angle tertiles were 4%, 6%, and 10% after 30 days (P < 0.001) and 24%, 31%, and 43% after 2 years (P < 0.001). After adjustment for clinical, laboratory, and ECG parameters, the QRS-T angle remained an independent predictor for 2 year mortality with a 4% increase in mortality for every 20° increase in QRS-T angle (P = 0.02).

CONCLUSIONS

The QRS-T angle is a readily available and inexpensive marker that can assist in the discrimination between ADHF and non-cardiac causes of acute dyspnoea and may aid in the risk stratification of these patients.